Habitual sentences (e.g. Bill smokes.) generalize an event over time, but how can you know a habitual sentence is true? We develop a computational model and use this to guide experiments into the truth conditions of habitual language. In Expts. 1 & 2, we measure participants’ prior expectations about the frequency with which an event occurs and validate the predictions of the model for when a habitual sentence is acceptable. In Expt. 3, we show that habituals are sensitive to top-down moderators of expected frequency: It is the expectation of future tendency that matters for habitual language. This work provides the mathematical glue between our intuitive theories’ of others and events and the language we use to talk about them.